similar to: results of knn() command

Hello R group Do you know if an EM algorithm exits for R to estimate missing data in a sample? I just found knn algorithm in to the package emv but it doesn't look to be the usual EM algorithm. Thanks Xavier -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info",

Maybe you should show your colleague how to access help pages in R? Right in ?knn, it says: prob: If this is true, the proportion of the votes for the winning class are returned as attribute 'prob'. so 1.0 mean all three NNs are of the `winning'; i.e., predicted, class, and 0.66667 means 2 out of the 3 NNs are of the winning class, etc. Andy > From: Ko-Kang

Hello. I found the behavior of knn( http://stat.ethz.ch/R-manual/R-devel/library/class/html/knn.html) function looking very strange. Consider the toy example. > library(class) > train <- matrix(nrow=5000,ncol=2,data=rnorm(10000,0,1)) > test <- matrix(nrow=10,ncol=2,data=rnorm(20,0,1)) > cl <- rep(c(0,1),2500) > knn(train,test,cl,1) [1] 1 1 0 0 1 0 1 1 0 1 Levels: 0 1 It

Dear list, I have two dissimilarity matrices, one for a training data set which I then clustered using PAM. The second is a diss matrix for a validation data set (an independent field sample). I have been trying to use knn to distinguish distances between the validation data set and the 6 mediods of the training data defined by using PAM. I continue to get error messages in regards to either the

ear all, This is my first time using this listserv and I am seeking help from the expert. OK, here is my question, I am trying to use impute.knn function in impute library and when I tested the sample code, I got the error as followingt: Here is the sample code: library(impute) data(khanmiss) khan.expr <- khanmiss[-1, -(1:2)] ## ## First example ## if(exists(".Random.seed"))

The function knn.cv in the class package doesn't have error checking to ensure that the length of the classlabel argument is equal to the number of rows in the test set. If the classlabel is short, the result is often a segfault. > library(class) > dat <- matrix(rnorm(1000), nrow=10) > cl <- c(rep(1,5), rep(2,5)) > cl2 <- c(rep(1,5), rep(2,4)) > knn.cv(dat, cl) [1] 2

Hi, I was trying to get the optimal 'k' for the knn. To do this I was using the following function : knn.cvk <- function(datmat, cl, k = 2:9) { datmatT <- (datmat) cv.err <- cl.pred <- c() for (i in k) { newpre <- as.vector(knn.cv(datmatT, cl, k = i)) cl.pred <- cbind(cl.pred, newpre) cv.err <- c(cv.err, sum(cl != newpre)) }

could someone help me to write a fonction doing an exponential smoothing in case of a multivariate time serie? I tried ewma <- function (x, lambda = 1, init = 0) { if (is.ts(x)) filter(lambda*x, filter=1-lambda, method="recursive", init=init) else stop(message="first argument should be a time serie") } but I can't apply that to multivariate Thanks

Hello, I am feeling that this question can have a very simple answer, but I can't find it. I need to use the function knn.dist from knnflex library. Whatever I try, I get the error: Error in as.vector.dist(x, "character") : unused argument(s) ("character") First example: > a<-NULL > a<-rbind(a,c(5.2,-8.1)) > a<-rbind(a,c(8.8,-16.1)) >

hello sir im working on text classification using java and r programming i start with exporting a document term matrix (tdm) from my java programme using corpus and now i try to apply knn algorithem using the matrix and r but i cant do that any help sir here my data here my script https://mega.nz/#!Q6J2ibAA!4PadiOKbP7rLodyiRrVsdKl-D2ZP7LYm0gaz94uBmF8 itry to post put icant whay!!

Hello, I've got a problem using the function "running" in package gregmisc For example: test<-c(1,2,3,4,5) running(test,fun=var,width=3) gives 1:1 1:2 1:3 2:4 3:5 NA NA 2 3 4 which is wrong because var(test[1:3]) [1] 1 Where am I wrong? Thanks Xavier -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

I am trying to use knn to do a nearest neighbor classification. I tried using my dataset and got an error message so I used a simple example to try and understand what I was doing wrong and got the same message. Here is what I typed into R: try [,1] [,2] [,3] [,4] r "A" "A" "T" "G" r "A" "A" "T" "G" f

Hi I have a dataset from biological data with forty samples whichh relate to four different treatments. Each sample has thousands of values but as usuual contains missing values I want to use knn to imput these missing values. I am doing tthis using knn.impute. Do I need to specify the various groups or can I just use the knn.impute command on the whole dataset together. Also I am setting

I am currently doing some prediction work using the knn script in the 'class' package. Does anyone know a way of having R return the IDs (sample IDs, or column IDs of the training matrix) of the 'k' samples that are chosen by the algorithm as being nearest to a given test sample? I have searched/read everything I can about the script, however have not found anything other than the

Hi Everybody, i again have the problem in using tune.knn(), its giving an error saying missing values are not allowed.... again here is the script for BreastCancer Data, library(e1071) library(mda) trdata<-data.frame(train,row.names=NULL) attach(trdata) xtr <- subset(trdata, select = -Class) ytr <- Class bestpara <-tune.knn(xtr,ytr, k = 1:25, tunecontrol = tune.control(sampling

How can I do a simple k nearest neighbor regression in R? My training data have 1 predictor and 1 outcome, both are numeric. I also need to use FPE and SC to find the optimal model. I know there is knn() in class package, but it's for knn classification. I also find a kknn package. What function should I use? Thanks in advance! Menghui

Could anybody help me out please? > cl<-as.factor(traindata[,13]) > knn(traindata[1:295,2], newdata[1:32,2], cl,k=2, prob=TRUE) Error in knn(traindata[1:295, 2], newdata[1:32, 2], cl, k = m, prob = TRUE) : Dims of test and train differ Both traindata and newdata have 13 elements. Only one of the first 12 elemnets is needed to predict the 13 element. What's the problem of

Hello all, am a newby in R, am trying the knn function, and am doing just a stupid test : > knn(c(1,2,3,4,5,6), c(3), k=4 ,prob=TRUE,factor(c(1:6))) the result is unstable !! i have each time different result : [1] 5 attr(,"prob") [1] 0.1666667 Levels: 1 2 3 4 5 6 [1] 4 attr(,"prob") [1] 0.1666667 Levels: 1 2 3 4 5 6 [1] 1 attr(,"prob") [1] 0.1666667 Levels:

Hi I need some help with ploting the ROC for K-nearest neighbors. Since KNN is a non-parametric classification methods, the predicted value will be either 0 or 1. It will not be able to test for different cutoff to plot ROC. What is the package or functions I should use to plot ROC for KNN? Thanks. Qian [[alternative HTML version deleted]]

hi, r users i have a problem with KNN. i have 2 datasets, X0 and X1. >dim(X0) >1471*13 dim(X1) >5221*13 and for every instances in the dataset X1, i want to find the nearest neighbour(1nn) in the dataset X0. and i dont have the true classifications of dataset X1. but the function knn() need true classifications(cl) to do prediction. i just curious if there are some other function